Protein engineering endeavors to design new peptides and proteins or to change the structural and/or functional characteristics of existing ones for specific purposes, opening the way for the development of new drugs. This work develops in a comprehensive way the theoretical formulation for the methods used in computer-assisted modeling and predictions, starting from the basic concepts and proceeding to the more sophisticated methods, such as Monte Carlo and molecular dynamics. An evaluation of the approximations inherent to the simulations will allow the reader to obtain a perspective of the possible deficiencies and difficulties and approach the task with realistic expectations. Examples from the authors laboratories, as well as from the literature provide useful information.

Protein engineering endeavors to design new peptides and proteins or to change the structural and/or functional characteristics of existing ones for specific purposes, opening the way for the development of new drugs. This work develops in a comprehensive way the theoretical formulation for the methods used in computer-assisted modeling and predictions, starting from the basic concepts and proceeding to the more sophisticated methods, such as Monte Carlo and molecular dynamics. An evaluation of the approximations inherent to the simulations will allow the reader to obtain a perspective of the possible deficiencies and difficulties and approach the task with realistic expectations. Examples from the authors laboratories, as well as from the literature provide useful information.

The third volume in the series on Computer Simulation of Biomolecular Systems continues with the format introduced in the first volume [1] and elaborated in the second volume [2]. The primary emphasis is on the methodological aspects of simulations, although there are some chapters that present the results obtained for specific systems of biological interest. The focus of this volume has changed somewhat since there are several chapters devoted to structure-based ligand design, which had only a single chapter in the second volume. It seems useful to set the stage for this volume by quoting from my preface to Volume 2 [2]. "The long-range 'goal of molecular approaches to biology is to describe living systems in terms of chemistry and physics. Over the last fifty years great progress has been made in applying the equations representing the underlying physical laws to chemical problems involv ing the structures and reactions of small molecules. Corresponding studies of mesoscopic systems have been undertaken much more recently. Molecular dynamics simulations, which are the primary focus of this volume, represent the most important theoretical approach to macromolecules of biological interest." ...

Scientific understanding as well as the way of studying science has been greatly changed since the advent of computer modeling. Computer simulation has played a central role in bridging theoretical and experimental studies. In this work, computer simulations were applied to explore biological systems on both protein folding and protein structure prediction studies. In the first study, the folding mechanisms of two alanine based helical peptides (Fs-21 peptide and MABA bonded Fs-21 peptide) were investigated by all atom molecular dynamics simulations and compared with experimental results. Multi-phase folding processes were observed for both peptides. Temperature change affected the relative stability of different ensembles. Helix-turn-helix conformation was found to be the most populated state at 300K while the full helix became more stable at low temperature (273K). The turn structure was found to be stabilized mainly by hydrophobic interactions. In the second study, helix-coil transition theory was elaborately tested by both statistical and energetic methods based on simulations of alanine based peptides. A weighted Ising model was proposed, and the model-derived propagation constant agreed very well with the experimental results. Solvation effect and electrostatic interactions were found to be the two main contributors to helix-coil transition. The results challenged the classic helix-coil transition theory by proving that the single sequence assumption was not appropriate for helix-coil transition. Conformational sampling has been a long-standing issue in computational sciences. In the third study, we systematically tested the convergence of the Replica Exchange Molecular Dynamics method (REMD), which is a recently developed method for conformational sampling enhancement. The results suggested that REMD can significantly enhance the sampling efficiency and accurately reproduce the long-time MD results with high efficiency. However, fluctuations at low temperatures (300 K) indicated that REMD simulations did not converge within our simulation time (14 ns). Much longer REMD simulation time might be needed for the system to reach thermodynamic equilibrium than expected. Finding the optimal side chain packing is a common issue in structure prediction, protein design and protein docking. In the fourth study, a new method was presented. The method overcame the rough energy landscape problem and enabled all-atom MD simulation to be applied directly to protein structure refinement. The method showed very successful results on buried side-chain assignments, nearly 100% accuracy on all 6 randomly picked proteins was reached; the results also clearly demonstrated that the proposed method can significantly enhance conformational sampling. These encouraging results suggested prospective applications on many other protein related systems.

In much of biology, the search for understanding the relation between structure and function is now taking place at the macromolecular level. Proteins, nucleic acids, and polysaccharides are macromolecule--polymers formed from families of simpler subunits. Because of their size and complexity, the polymers are capable of both inter- and intramolecular interactions. These interactions confer upon the polymers distinctive three-dimensional shapes. These tertiary configurations, in turn, determine the function of the macromolecule. Computers have become so inextricably involved in empirical studies of three-dimensional macromolecular structure that mathematical modeling, or theory, and experimental approaches are interrelated aspects of a single enterprise.

This volume provides computational methods and reviews various aspects of computational studies of protein aggregation. Chapters discuss the relationship between protein misfolding and protein aggregation, methods of prediction of aggregation propensities of protein, peptides, protein structure, results of computer simulations of aggregation, and computational simulations focused on specific diseases such as Alzheimer’s, Parkinson’s, and preeclampsia. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computer Simulations of Aggregation of Proteins and Peptides aims to ensure successful results in the further study of this vital field.

Volume Two of this two-volume sequence presents a comprehensive overview of protein structure prediction methods and includes protein threading, De novo methods, applications to membrane proteins and protein complexes, structure-based drug design, as well as structure prediction as a systems problem. A series of appendices review the biological and chemical basics related to protein structure, computer science for structural informatics, and prerequisite mathematics and statistics.

The rapidly evolving field of protein science has now come to realize the ubiquity and importance of protein-protein interactions. It had been known for some time that proteins may interact with each other to form functional complexes, but it was thought to be the property of only a handful of key proteins. However, with the advent of high throughput proteomics to monitor protein-protein interactions at an organism level, we can now safely state that protein-protein interactions are the norm and not the exception. Thus, protein function must be understood in the larger context of the various binding complexes that each protein may form with interacting partners at a given time in the life cycle of a cell. Proteins are now seen as forming sophisticated interaction networks subject to remarkable regulation. The study of these interaction networks and regulatory mechanism, which I would like to term "systems proteomics," is one of the thriving fields of proteomics. The bird-eye view that systems proteomics offers should not however mask the fact that proteins are each characterized by a unique set of physical and chemical properties. In other words, no protein looks and behaves like another. This complicates enormously the design of high-throughput proteomics methods. Unlike genes, which, by and large, display similar physico-chemical behaviors and thus can be easily used in a high throughput mode, proteins are not easily amenable to the same treatment. It is thus important to remind researchers active in the proteomics field the fundamental basis of protein chemistry. This book attempts to bridge the two extreme ends of protein science: on one end, systems proteomics, which describes, at a system level, the intricate connection network that proteins form in a cell, and on the other end, protein chemistry and biophysics, which describe the molecular properties of individual proteins and the structural and thermodynamic basis of their interactions within the network. Bridging the two ends of the spectrum is bioinformatics and computational chemistry. Large data sets created by systems proteomics need to be mined for meaningful information, methods need to be designed and implemented to improve experimental designs, extract signal over noise, and reject artifacts, and predictive methods need to be worked out and put to the test. Computational chemistry faces similar challenges. The prediction of binding thermodynamics of protein-protein interaction is still in its infancy. Proteins are large objects, and simplifying assumptions and shortcuts still need to be applied to make simulations manageable, and this despite exponential progress in computer technology. Finally, the study of proteins impacts directly on human health. It is an obvious statement to say that, for decades, enzymes, receptors, and key regulator proteins have been targeted for drug discovery. However, a recent and exciting development is the exploitation of our knowledge of protein-protein interaction for the design of new pharmaceuticals. This presents particular challenges because protein-protein interfaces are generally shallow and interactions are weak. However, progress is clearly being made and the book seeks to provide examples of successes in this area.

The book gives a comprehensive review of the most advanced multiscale methods for protein structure prediction, computational studies of protein dynamics, folding mechanisms and macromolecular interactions. It approaches span a wide range of the levels of coarse-grained representations, various sampling techniques and variety of applications to biomedical and biophysical problems. This book is intended to be used as a reference book for those who are just beginning their adventure with biomacromolecular modeling but also as a valuable source of detailed information for those who are already experts in the field of biomacromolecular modeling and in related areas of computational biology or biophysics.